Comments (3)
Right now the space of fit parameters is R^N without any bounds (bounds are applied in the fit, but these are mostly arbitrary). Right now we do indeed scale that space via number of sigmas (e.g. up/nominal/down are at 1,0,-1 respectively, but I think this is mostly an implementation detail. It is the job of the analyzer to specify at which floating point value (using units s/he determines are best) the histograms live. I think for histbook it's enough to use R^n or string^n and leave the units to the user (or alternatively fix a unit system and then it's the job of the user to provide the right floats)
There might also be a non-trivial condition on the validity of the interpolation/extrapolation.. but that is probably the job of the interpolation to handle (eg. only some subspace of R^n might be valid) trying to request a histogram outside of that subvolume should raise an error/exception.
from histbook.
The reason I ask is because one of histbook's methods, Hist.fraction
, makes an efficiency plot with a choice of binomial statistics (e.g. Wilson, Clopper-Pearson, Feldman-Cousins, etc.). One of the inputs to this calculation is a user-specified "number of sigmas" or "confidence level" where the user would like to have the asymmetric error bounds quoted. So far, I've been using confidence level because it's conceptually more relevant for arbitrary probability distributions, but it means that the default value is erf(sqrt(0.5))
(one sigma).
I'm trying to decide which are the right "units" for the statistical error space. If you're using number of sigmas n
in R^N and nobody's complained that computing confidence levels with CL = erf(sqrt(0.5*n))
goes through a Gaussian assumption that might not be valid, then I'll do it, too. R^N is much easier to work with than (0, 1)^N and if a Gaussian assumption is not valid, then we just say that erf(sqrt(0.5*n))
is a "convenient transformation function" that we use to scale confidence levels.
from histbook.
Apart from the discussion about sigmas versus confidence levels, this is actually #20, which is done.
from histbook.
Related Issues (20)
- Plotting normalised histograms HOT 3
- Fill from Pandas DataFrame HOT 1
- Get rid of expression labels HOT 1
- List of input variables HOT 2
- Sensible plotting defaults for trivial cases HOT 3
- Logscale not working properly HOT 16
- Category color changes in stack() HOT 13
- statelessness has changed with logscale HOT 1
- User defined binning HOT 4
- NameError: global name 'threading' is not defined HOT 1
- filling is slower with numpy 1.15 HOT 2
- Plotting histos and saving to pdf/png in script HOT 1
- logarithmic scale on Y axis of Stack histogram HOT 5
- Implementation of export to TH2 HOT 4
- histbook/Spark questions HOT 20
- Improved support for binned data in Vega-Lite HOT 22
- Multiple object output in JupyterLab HOT 6
- Efficiency diagram HOT 5
- Variable bin width histograms HOT 1
- Update to Vega-Lite 3
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from histbook.